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Change detection based on features invariant to monotonic transforms and spatially constrained matching

[+] Author Affiliations
Marco Túlio A. N. Rodrigues, Daniel Balbino de Mesquita, Erickson R. Nascimento, William Robson Schwartz

Universidade Federal de Minas Gerais, Department of Computer Science, Av. Antonio Carlos 6627, Belo Horizonte, MG 31270-010, Brazil

J. Electron. Imaging. 25(1), 013001 (Jan 05, 2016). doi:10.1117/1.JEI.25.1.013001
History: Received July 5, 2015; Accepted December 2, 2015
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Abstract.  In several image processing applications, discovering regions that have changed in a set of images acquired from a scene at different times and possibly from different viewpoints plays a very important role. Remote sensing, visual surveillance, medical diagnosis, civil infrastructure, and underwater sensing are examples of such applications that operate in dynamic environments. We propose an approach to detect such changes automatically by using image analysis techniques and segmentation based on superpixels in two stages: (1) the tuning stage, which is focused on adjusting the parameters; and (2) the unsupervised stage that is executed in real scenarios without an appropriate ground truth. Unlike most common approaches, which are pixel-based, our approach combines superpixel extraction, hierarchical clustering, and segment matching. Experimental results demonstrate the effectiveness of the proposed approach compared to a remote sensing technique and a background subtraction technique, demonstrating the robustness of our algorithm against illumination variations.

© 2016 SPIE and IS&T

Citation

Marco Túlio A. N. Rodrigues ; Daniel Balbino de Mesquita ; Erickson R. Nascimento and William Robson Schwartz
"Change detection based on features invariant to monotonic transforms and spatially constrained matching", J. Electron. Imaging. 25(1), 013001 (Jan 05, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.1.013001


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